Spatial and Temporal Calibration of Self-Built Panoramic LiDAR in Targetless Environments

Compared to multiline light detection and ranging (LiDAR), panoramic LiDAR offers a larger field of view (FOV) and is widely used for tracking and mapping applications. Accurate calibration is essential for acquiring precise panoramic point clouds. However, the current calibration methods often depend on a calibration board and overlook the time offset between sensors. To address these limitations, we propose a novel spatiotemporal calibration method for panoramic LiDAR in targetless environments. In our approach, the extrinsic parameters are initially estimated based on plane alignment, enabling calibration without strict initial parameter settings, thereby enhancing robustness. Next, we jointly optimize the LiDAR point-to-plane and angular velocity factors to accurately determine the LiDAR-motor extrinsic parameters and time offset, all without requiring a specific target object. We evaluated the proposed calibration method through extensive experiments in both simulated and real-world environments, examining the influence of time offset, initial sensor setup, motor speed, and LiDAR noise on calibration performance. The code will be made publicly available at: https://github.com/QLJX/Calib_PanoramicLiDAR.